Along with current advance of technology, information apparatuses such as personal computers (PCs), and information appliances such as television receivers, and other audio-visual apparatuses have been developed and are commercially available. Consumers purchase these information apparatuses directly from show cases in stores, or online shop them.
Purchase information of what products the consumers have bought, and history of use and operation of the products by the users may serve as source information for value addition to be used to predict consumer life and needs of the consumers. For example, purchasers of television sets are expected to desire to receive signals from broadcasting satellites (or the purchasers of television sets may be interested in satellite broadcasting more than purchasers of other products). The purchase information of the purchasers of the television sets may be registered in a purchase information data base and the data base is then analyzed to select addressees to which advertising catalogs for satellite broadcasting may be efficiently sent. Based on television viewing information (for example, of what programs are viewed for how long), it is possible to determine what category of television programs each individual prefers. Automatic reception service of advertising contents may be provided to people who like the same category.
The method for linking the purchasers of the television sets to the advertising catalogs of satellite broadcasting needs human intervention in the interpretation of the data, and cannot be automatically adapted to a variety of other services.
The method of distributing programs of the same category based on the television viewing information requires less data interpretation because the object from which information is collected is identical to the object to which value-added information is provided. Broadcast and distributed contents need to be automatically categorized, and this method cannot be automatically adapted to a variety of other services.
The provision of service based on the user's preference has conventionally required human intervention in data interpretation or automatic categorization of the contents. Applying the methods in vast amount of service is costly, and is difficult to technically implement.